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Abstract Eukaryotic Argonaute proteins (eAgos) utilize short nucleic acid guides to target complementary sequences for RNA silencing, while prokaryotic Agos (pAgos) provide immunity against invading plasmids or bacteriophages. The Sir2-domain associated short pAgo (SPARSA) immune system defends against invaders by depleting NAD+ and triggering cell death. However, the molecular mechanism underlying SPARSA activation remains unknown. Here, we present cryo-EM structures of inactive monomeric, active tetrameric and active NAD+-bound tetrameric SPARSA complexes, elucidating mechanisms underlying SPARSA assembly, guide RNA preference, target ssDNA-triggered SPARSA tetramerization, and tetrameric-dependent NADase activation. Short pAgos form heterodimers with Sir2-APAZ, favoring short guide RNA with a 5′-AU from ColE-like plasmids. RNA-guided recognition of the target ssDNA triggers SPARSA tetramerization via pAgo- and Sir2-mediated interactions. The resulting tetrameric Sir2 rearrangement aligns catalytic residue H186 for NAD+ hydrolysis. These insights advance our understanding of Sir2-domain associated pAgos immune systems and should facilitate the development of a short pAgo-associated biotechnological toolbox.

Abstract Background Work-related musculoskeletal disorders (WMSDs) show a rapid growth trend. It has brought a huge economic burden to the society and become a serious occupational health problem that needs to be solved urgently. This study aimed to analyze the local muscle response under continuous ergonomic load, screen sensitive fatigue-related biomarkers and provide data support for the early prevention of local muscle damage and the exploration of early warning indicators. Methods Thirteen male college student volunteers were recruited to perform simulated repetitive manual lifting tasks in the laboratory. The lifting task was designed for 4 periods which lasted for 12 min in each, and then paused for 3 min for sampling. Local muscle fatigue is assesed by the Rating of perceived exertion (RPE) and the Joint analysis of sEMG spectrum and amplitude (JASA). Elbow venous blood was collected and 14 kinds of biomarkers were analyzed, which included Metabolic markers Ammonia (AMM), Lactic acid (LAC), Creatine kinase (CK), Lactate dehydrogenase (LDH), Cartilage oligomeric matrix protein (COMP), C-telopeptide of collagen I and II (CTX-I, CTX-II) and Calcium ion (Ca2+); Oxidative stress marker Glutathione (GSH); Inflammatory markers C-reaction protein (CRP), Prostaglandin E2 (PG-E2), Interleukin-6 (IL-6) and Tumor necrosis factor α (TNF-α); Pain marker Neuropeptide Y (NPY). Repeated measures analysis of variance (Repeated ANOVA), linear regression analysis, t-test and spearman correlation analysis were used to analyze the data. Results Both subjective and objective fatigue appeared at the same period. Serum AMM, LAC, CK, LDH, COMP, CTX-II, Ca2+ and NPY after fatigue were significantly higher than those before fatigue (p

Abstract Background Higher education students exhibit heightened sensitivity to environmental changes as they navigate the critical transition from adolescence to adulthood. The coronavirus disease 2019 (COVID-19) pandemic has posed unprecedented challenges to universities worldwide, exemplifying a crisis that profoundly affects the learning outcomes and psychological status of college students. Although it is known that campus lockdown has triggered dramatic changes in lifestyles, learning outcomes, and psychological statuses, in-depth knowledge of the causal relationships among these changes remains largely unclear. Methods Here, we conducted a cross-sectional survey designed to assess the impact of campus closure during COVID-19 on lifestyle, educational performance, and anxiety levels among college students. We surveyed over 3,500 junior college, undergraduate and graduate participants from 94 colleges/universities across 30 provinces, municipalities, and autonomous regions in China.We employed structural equation modeling (SEM) to explore the relationships between changes in lifestyle, educational performance, and levels of anxiety associated with campus open or closure regulations during the COVID-19 pandemic. Results Our results discovered that sleep duration, physical activity involvement, and social connections were crucial for sustaining students' learning outcomes and mental health. The shift to online learning and campus lockdown exacerbated stressors, contributing to heightened anxiety (β = 0.066), disrupted sleep patterns, and enhanced physical activity (β = 0.070) and reduced learning effect (β = -0.059). Sleep patterns were disrupted by the campus lockdown, an effect mediated by the degradation of relationships among classmates. Nonetheless, the best-fitting SEM uncovered the intricate relationships among lifestyle changes, learning outcomes, and psychological status in response to sudden environmental changes (Fisher’s C = 80.949, P = 0.328). These results highlight the critical role of adaptable, supportive campus policies tailored to meet the diverse needs and interests of students during and beyond crises (Fisher’s C = 59.568, P = 0.809). Conclusions Our study advocates for a holistic approach that addresses the multifaceted aspects of student life to cultivate a resilient academic community. This approach contributes to a deeper understanding of the effects of sudden environmental changes on students' psychological well-being and academic performance in the post-pandemic era.

BackgroundShoemaking industry workers are prone to work-related musculoskeletal disorders (WMSDs) due to long-term awkward postures during the work process. There is little research on the prevalence and influencing factors of WMSDs in the knee region of this industry, and it should be taken seriously. ObjectiveTo estimate the prevalence of work-related knee pain among shoemaking workers and analyze the related influencing factors. MethodsA total of 6982 shoemaking workers were selected from 26 shoemaking factories in Guangdong, Hubei, Fujian, Chongqing, Shandong, Zhejiang, and Jingxi by convenience sampling. Prevalence of work-related knee pain in past year, demographic characteristics, occupational related factors, and work posture were collected by a cross-sectional survey using the electronic version of Musculoskeletal Disorder Questionnaire. Logistic regression analysis was used to analyze the influencing factors that may lead to work-related knee pain.ResultsThis survey collected 6982 valid questionnaires with a recovery rate of 98.3%. The prevalence of work-related knee pain of shoemaking workers in the past 12 months was 13.0% (908/6982). According to the results of logistic regression analysis, compared with workers with less than 5 years of service, workers with 5-10 years of service (OR=1.21, 95%CI: 1.02, 1.45) and more than 10 years (1.53, 95%CI: 1.27, 1.83) showed a higher risk of knee WMSDs; sometimes, often and very frequent (reference : rarely or never) long-term standing (OR=1.33, 95%CI: 1.08, 1.64; OR=2.67, 95%CI: 2.10, 3.39; OR=2.75, 95%CI: 2.08, 3.63) and sometimes, often and very frequent (reference: rarely or never) long-term squatting or kneeling (OR=1.80, 95%CI: 1.47, 2.21; OR=2.43, 95%CI: 1.58, 3.75; OR=3.22, 95%CI: 1.66, 6.24) increased the risk of knee pain: long-term bending (OR=1.59, 95%CI: 1.34, 1.89) and often repeated movement of lower limbs and ankles (OR=1.48, 95%CI: 1.25, 1.75) were also risk factors for knee WMSDs among shoemaking industry workers (P

Highlights Developing a universal strategy of the p–n homojunction engineering that could significantly boost electron mobility of electron transport layer (ETL) by two orders of magnitude. Proposing a new mechanism based on p–n homojunction to explain inhibited carrier loss at buried interface. Setting a new performance benchmark as high as 25.50% for planar perovskite solar cells employing TiO2 as ETLs.

Abstract Silent information regulator 2 (Sir2) proteins typically catalyze NAD+-dependent protein deacetylation. The recently identified bacterial Sir2 domain-containing protein, defense-associated sirtuin 2 (DSR2), recognizes the phage tail tube and depletes NAD+ to abort phage propagation, which is counteracted by the phage-encoded DSR anti-defense 1 (DSAD1), but their molecular mechanisms remain unclear. Here, we determine cryo-EM structures of inactive DSR2 in its apo form, DSR2–DSAD1 and DSR2–DSAD1–NAD+, as well as active DSR2–tube and DSR2–tube–NAD+ complexes. DSR2 forms a tetramer with its C-terminal sensor domains (CTDs) in two distinct conformations: CTDclosed or CTDopen. Monomeric, rather than oligomeric, tail tube proteins preferentially bind to CTDclosed and activate Sir2 for NAD+ hydrolysis. DSAD1 binding to CTDopen allosterically inhibits tube binding and tube-mediated DSR2 activation. Our findings provide mechanistic insight into DSR2 assembly, tube-mediated DSR2 activation, and DSAD1-mediated inhibition and NAD+ substrate catalysis in bacterial DSR2 anti-phage defense systems.

We introduce a multimodal rapid identification and growth status discrimination method for morchella. Based on the unique biological characteristics and growth environmental requirements of morchella, the efficient and accurate identification of key growth stages of morchella is achieved through the integration of multimodal information acquisition technology. During the rapid identification process of the growth stage of Morchella, the Multi Stage Vision Enhanced Position Encoding Vision Transformer (MS-EP ViT) model is adopted. By introducing multi-stage input embedding, enhanced position encoding, and optimized Transformer Encoder layers, the performance of the model in identifying different growth stages of Morchella mushrooms is significantly improved. In the multimodal Morchella growth state discrimination method, text and image modalities are integrated, a Non downsampled Contourlet Transform Mask Region based Convolutional Neural Network (NSCT Mask R-CNN) model is designed, and a multimodal feature extraction strategy combining Non downsampled Contourlet Transform (NSCT) features with environmental features is explored. This strategy effectively achieves the goals of object detection and instance segmentation, and thus we have accurately evaluated the growth status of Morchella in the later stages of mulberry, young mushroom, and mature. The experimental results show that both models have achieved significant improvements in recognition accuracy and stability, and the rationality of hyperparameter settings has been verified through convergence and parameter sensitivity experiments. Overall, we provide a more accurate and efficient identification method for monitoring the growth of Morchella, which helps to better understand the growth of Morchella and provides scientific basis for optimizing its growth environment.

Laboratory experiments are one of the important means used to investigate travel choice behavior under strategic uncertainty. Many experiment-based studies have shown that the Nash equilibrium can predict aggregated route choices, while the fluctuations, whose mechanisms are still unclear, continue to exist until the end. To understand the fluctuations, this paper proposes a route-dependent attraction-based stochastic process model, which shares exactly the same behavioral foundation introduced in Part I of the study (Qi et al., 2023), i.e., route-dependent inertia and route-dependent preference. The model predictions are carefully compared with the experimental observations obtained from the congestible parallel-route laboratory experiments containing 312 subjects and eight decision-making scenarios (Qi et al., 2023). The results show that the proposed stochastic process model can precisely reproduce the random oscillations both in terms of flow switching and route flow evolution. Subsequently, an approximated model is developed to enhance the efficiency in evaluating the equilibrium distribution, providing a practical tool to evaluate the impacts of transportation policies in both long- and short-term runs. To the best of our knowledge, this paper is the first attempt to model and explain experimental phenomena by introducing stochastic process theories, as well as a successful example of applying experimental economics methodology to improve our understanding of human travel choice behavior.

This paper investigates the impact of surface effects on the propagation behavior of longitudinal waves in a nanorod. A theoretical model has been established on the basis of a newly proposed theory of elastic waves with surface effects. The surface effects comprise two components: the effect of surface energy and the effect of surface inertia. An analytical formula for the longitudinal wave velocity of a nanorod has been derived. Two inherent lengths at nanoscale have been deduced to characterize these two types of surface effects. The results indicate that the longitudinal wave in a nanorod is still nondispersive. However, an attractive phenomenon uncovered is that when the size of a rod reduces to the inherent lengths at nanoscale, the longitudinal wave velocity becomes size-dependent due to the effects of surface energy and surface inertia. The former increases the longitudinal wave velocity, whereas the latter decreases it. This can be understood as the former equivalently increasing the stiffness of the nanorod, whereas the latter enhancing its effective density. On the other hand, when the rod is at the macroscale, the longitudinal wave velocity degenerates to the classical velocity for a macroscopic rod without any surface effects. The current findings not only enhance our understanding of the size-dependent wave velocity of longitudinal waves in nanorods but also facilitate precisely designing the elastic wave nanodevices.

Abstract Thermoelectric materials are highly promising for waste heat harvesting. Although thermoelectric materials research has expanded over the years, bismuth telluride‐based alloys are still the best for near‐room‐temperature applications. In this work, a ≈38% enhancement of the average ZT (300−473 K) to 1.21 is achieved by mixing Bi0.4Sb1.6Te3 with an emerging thermoelectric material Sb2Si2Te6, which is significantly higher than that of most BiySb2−yTe3‐based composites. This enhancement is facilitated by the unique interface region between the Bi0.4Sb1.6Te3 matrix and Sb2Si2Te6‐based precipitates with an orderly atomic arrangement, which promotes the transport of charge carriers with minimal scattering, overcoming a common factor that is limiting ZT enhancement in such composites. At the same time, high‐density dislocations in the same region can effectively scatter the phonons, decoupling the electron‐phonon transport. This results in a ≈56% enhancement of the thermoelectric quality factor at 373 K, from 0.41 for the pristine sample to 0.64 for the composite sample. A single‐leg device is fabricated with a high efficiency of 5.4% at ΔT = 164 K further demonstrating the efficacy of the Sb2Si2Te6 compositing strategy and the importance of the precipitate‐matrix interface microstructure in improving the performance of materials for relatively low‐temperature applications.

The hematopoietically-expressed homeobox gene (HHEX) played a critical role in regulating the immune system that the abnormality of which was involved in the psychopathology and cognitive deficits of psychiatric disorders. The aim of this study was to investigate the effect of HHEX rs1111875 polymorphism on the susceptibility and cognitive deficits of first-episode schizophrenic patients (FSP). We assessed cognitive function in 239 first-episode patients meeting DSM-IV for schizophrenia, and 368 healthy controls using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). The HHEX rs1111875 polymorphism was genotyped. Our results showed that the allelic and genotypic frequencies of HHEX rs1111875 polymorphism didn't differ between FSP and healthy controls (both p > 0.05) after adjusting for sex and age. Cognitive test scores in FSP were significantly lower than those in healthy controls on all scales (all p 0.05) after adjusting for covariates. There was a significant genotype (p 0.05) effect on the delayed memory score after adjusting for covariates. The HHEX rs1111875 polymorphism was significantly associated with the delayed memory score in FSP (p 0.05) after adjusting for covariates. Our findings supported that the HHEX rs1111875 polymorphism did not contribute to the susceptibility to FSP. However, this polymorphism might influence the delayed memory in FSP. Moreover, FSP had poorer cognitive function than healthy controls except for the visuospatial/constructional domain.

Introduction: We investigated trends in the use of therapeutic drugs for pregnant patients with rheumatic diseases in nine Chinese cities (Beijing, Chengdu, Guangzhou, Harbin, Hangzhou, Shanghai, Shenyang, Tianjin, and Zhengzhou) to provide a reference for drug use in clinic.Methods: Outpatient prescription data for pregnant patients diagnosed with rheumatic diseases in nine cities across China in 2016–2021 were extracted from the Hospital Prescription Cooperation Project of the Hospital Pharmacy Professional Committee of the Chinese Pharmaceutical Association. A retrospective analysis was then performed, incorporating data on patient age, defined daily doses (DDDs), defined daily cost (DDC), and other metrics.Results: In 2016–2020, more than 70% of the pregnant patients diagnosed with rheumatic diseases in these nine cities were 25 to < 35 years of age. The most common rheumatic diseases during pregnancy were antiphospholipid antibody syndrome (APS) and systemic lupus erythematosus (SLE). In terms of the routine use of daily therapeutic drugs, the DDDs of low molecular weight heparins (LMWHs), glucocorticoids, and immunosuppressive agents dominated the top three. Intravenous immunoglobulin (IVIG) and tumor necrosis factor inhibitors (TNFi) have been used since 2019 and had been in the forefront of the DDC.Conclusion: The number and total cost of prescriptions for therapeutic drugs of pregnancy complicated by rheumatic diseases, have increased significantly over the study interval. Conventional therapeutic drugs, especially glucocorticoids, LMWHs, and hydroxychloroquine were the most widely used drugs in pregnant patients with rheumatic diseases. However, IVIG and TNFi, relatively high cost, have shown gradual increases in clinical use since 2019.

BackgroundLong-term exposure to whole-body vibration (WBV) will affect the health of occupational drivers. However, research on the characteristics of WBV exposure by urban bus drivers and health risk evaluation is still insufficient. ObjectiveTo identify the characteristics of occupational WBV exposure of bus drivers serving 31 bus routes provided by three branches of Haikou Public Transport Group, and to evaluate their occupational health risks related to WBV. MethodsA total of 31 out of 142 bus routes run by three branches of Haikou Public Transport Group were selected to monitor WBV exposure of 31 bus drivers during driving. WBV parameters such as triaxial frequency weighted acceleration (awx, awy, awz) and triaxial crest factor (CFx, CFy, CFz) of the drivers were determined with a six-channel human vibration meter. Two methods, 8-hour daily value of the weighted root mean square average weighted vibration [A(8)] based on aw and 8-hour daily value of vibration dose [VDV(8)] based on vibration dose value (VDV), were used for health risk assessment and classified WBV health risk results into three levels (high, medium, and low) by the exposure action value (EAV) and exposure limit values (ELV) for A(8) and VDV(8) recommended by ISO 2631-1:1997. The two evaluation methods, A(8) and VDV(8), were compared by Fisher's exact test. ResultsRegarding the WBV parameters, the vector sum of acceleration (av) was 0.321-0.680 m·s−2, the VDV of monitoring interval was 3.824-10.174 m·s−1.75, and the VDV(8) was 6.039-13.505 m·s−1.75; their values in mean ± standard deviation were (0.480±0.100) m·s−2, (6.987±2.737) m·s−1.75, and (9.773±4.540) m·s−1.75, respectively. Positive correlations were found between awx and awz, av and awz, CFx and CFy, CFy and CFz. No bus route's WBV exposure level was graded as high health risk by either A(8) or VDV(8). The number of routes graded as low health risk by A(8) was 26, while the number by VDV(8) was 12. The consistency rates of health risk levels evaluated by the two methods were 66.7% (6/9), 54.6% (6/11), and 45.5% (5/11) for the three bus group branches, respectively. The difference in WBV health risk assessment results between the two evaluation methods was not statistically significant. ConclusionPositive correlations are found between triaxial acceleration and triaxial crest factor. There is no difference in the results of using A(8) and VDV(8) to evaluate health risks of WBV in urban bus routes.

With the maturity of artificial intelligence (AI) technology, applications of AI in edge computing will greatly promote the development of industrial technology. However, the existing studies on the edge computing framework for the Industrial Internet of Things (IIoT) still face several challenges, such as deep hardware and software coupling, diverse protocols, difficult deployment of AI models, insufficient computing capabilities of edge devices, and sensitivity to delay and energy consumption. To solve the above problems, this paper proposes a software-defined AI-oriented three-layer IIoT edge computing framework and presents the design and implementation of an AI-oriented edge computing system, aiming to support device access, enable the acceptance and deployment of AI models from the cloud, and allow the whole process from data acquisition to model training to be completed at the edge. In addition, this paper proposes a time series-based method for device selection and computation offloading in the federated learning process, which selectively offloads the tasks of inefficient nodes to the edge computing center to reduce the training delay and energy consumption. Finally, experiments carried out to verify the feasibility and effectiveness of the proposed method are reported. The model training time with the proposed method is generally 30% to 50% less than that with the random device selection method, and the training energy consumption under the proposed method is generally 35% to 55% less.

Cliffs are essential refuges for endangered plants, but their inaccessibility and inadequate reliable survey methods make the assessment of the survival status of these plants difficult. We employed the drone-based Nap-of-the-Object Photography (NoOP) technique, which involves flying close to an object's surface to automatically capture high-precision images of the object surface, to investigate an endangered cliff plant, Clematis acerifolia, in North China. A drone (DJI M300 RTK) equipped with a visible-light mapping camera (DJI Zenmuse P1) was used for automated NoOP flights, enabling the collection and reconstruction of millimeter-level, high-precision three-dimensional (3D) models of the cliff plots. Across 12 cliffs, we identified and measured the precise coordinates and values of eight microtopographical and five morphological characteristic factors for 1444 C. acerifolia plants. The Ground sampling distance (GSD) of the high-precision 3D models reached 0.10 cm/pixel, and the mean value of the root mean squared error of the sampled positioning coordinates was 4.65 cm/pixel. Correlation and correspondence analyses were used for data analyses. The results indicated that the growth status of C. acerifolia was significantly influenced by a complex factor interaction, particularly plant altitude, crevice width, and crevice length. This method for cliff plant surveys using drone-based NoOP determines the parameters for such surveys and demonstrates the effectiveness and potential of this method for cliff plant investigations. The primary microtopographical factors affecting the growth and distribution status of C. acerifolia were identified, providing pointers for translocation conservation and propagation reintroduction of this endangered plant.

Aiming at a cognitive radio network (CRN) consisting of a pair of primary users and M pairs of secondary users, the secondary throughput maximization for CRN based on the non-linear energy harvesting model was studied.Specifically, in the case of considering secondary transmitter (ST) circuit power, the secondary throughput maximization (STM) problem with primary users’ throughput demands was first modeled as a non-linear optimization problem and then transformed into a convex optimization problem.Finally, a low-complexity algorithm combining the golden section and dichotomy was proposed.By applying this low-complexity algorithm, the optimal time allocation of the primary transmitter (PT)’s energy transmission and secondary users’ information transmission, and the optimal transmission power of PT were obtained.In addition, for the case of neglecting the ST circuit power, the convex property of the STM problem was first proved, and then a more efficient algorithm was designed to solve it.The simulation results show that compared with the equal time allocation method and the link gain priority method, the proposed design algorithm significantly improves the throughput of secondary users.

Fraud problems in loan application assessment cause significant losses for finance companies worldwide, and much research has focused on machine learning methods to improve the efficacy of fraud detection in some financial domains. However, diverse information falsification in individual fraud remains one of the most challenging problems in loan applications. To this end, we conducted an empirical study to explore the relationships between various fraud types and analyzed the factors influencing information fabrication. Weak relationships exist among different falsification types, and some essential factors play the same roles in different fraud types. In contrast, others have various or opposing effects on these types of frauds. Based on this finding, we propose a novel hierarchical multi-task learning approach to refine fraud-detection systems. Specifically, we first developed a hierarchical fraud category method to break down this problem into several subtasks according to the information types falsified by customers, reducing fraud identification's difficulty. Second, a heterogeneous network with a meta-path-based random walk and heterogeneous skip-gram model can solve the representation learning problem owing to the sophisticated relationships among the applicants' information. Furthermore, the final subtasks can be predicted using a multi-task learning approach with two prediction layers. The first layer provides the probabilities of general fraud categories as auxiliary information for the second layer, which is for specific subtask prediction. Finally, we conducted extensive experiments based on a real-world dataset to demonstrate the effectiveness of the proposed approach.

Government macro-control through various policies is an important way to mitigate air pollution and greenhouse gases. Therefore, environmental tax is used worldwide as an important measure. However, few studies have considered the interaction between carbon and environmental protection taxes. Additionally, different sectors differ in their energy structure, pollution emission intensity, and economic status, and previous studies rarely proposed differentiated environmental tax rates based at the sectoral level. A model framework combining the computable general equilibrium (CGE) model and Bayesian optimization (BO) algorithm is proposed to maximize GDP, meet environmental planning objectives, and explore the optimal environmental taxation scheme to realize the multi-objective optimization of the economy and environment. Meanwhile, this study compares the different impact mechanisms of environmental protection tax and carbon tax. It discusses the impacts of differentiated environmental tax rates in different sectors on the environment and economy. For example, the results show that the coordinated implementation of environmental protection and carbon tax policies and the sectoral differentiated environmental tax rates in China could better balance economic development and environmental governance. Additionally, the optimal taxation scheme could mitigate air pollution and greenhouse gases, promote economic growth, and realize sustainable economic and environmental development. Furthermore, the optimized taxation scheme positively affects the energy and industrial structures.